Biology subjects

Elagina, L.

Publications and source records attributed to Elagina, L..

3 recordsLinked to original sources

bioRxiv2021

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, but integrative models to predict clinical benefit using DNA or RNA alone have not been comprehensively explored. We sequenced DNA and RNA from melanoma patients receiving CPB, and aggregated previously published data, yielding whole exome sequencing data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole-exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences and found that patients whose melanomas have high TMB together with either high TCB or high BCB survived longer and had higher response rates as compared to patients with either low TMB or TCB/BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Mitogen- Activated Protein Kinase Kinase Kinase Kinase 1) combined with the transcription factor TBX3 (T-Box Transcription Factor 3) which is overexpressed in poorly differentiated melanomas. We conclude that RNA-based (MAP4K1&TBX3) or DNA-based (TCB&TMB) models combining immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

genomics↗

bioRxiv2020

Distinct evolutionary paths in chronic lymphocytic leukemia during resistance to graft-versus-leukemia

Resistance to the graft-versus-leukemia (GvL) effect remains the major barrier to successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) for aggressive hematologic malignancies. The basis of GvL resistance for advanced lymphoid malignancies remains incompletely understood. We hypothesized that for patients with chronic lymphocytic leukemia (CLL) treated with allo-HSCT, leukemic cell-intrinsic features shape GvL outcomes by directing the evolutionary trajectories of CLL cells. Integrated genetic, transcriptomic and epigenetic analyses of CLL cells from 10 patients revealed that the clinical kinetics of post- HSCT relapse are shaped by distinct molecular dynamics and suggest that the selection pressures of the GvL bottleneck are unlike those imposed by chemotherapy. No selective advantage for HLA loss was observed, even when present in pre-transplant subpopulations. Regardless of post-transplant relapse kinetics, gain of stem cell modules was a common signature associated with leukemia relapse. These data elucidate the biological pathways that underlie GvL resistance and post-transplant relapse. One Sentence SummaryWe find that the clinical kinetics of chronic lymphocytic leukemia relapse after stem cell transplant are underwritten by distinct genetic and epigenetic evolutionary trajectories and suggest that the selection pressures of the post-transplant, immunologic bottleneck are unlike those imposed by chemotherapy.

cancer biology↗

bioRxiv2018

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Driver mutations alter cells from normal to cancer through several evolutionary epochs: premalignancy, early malignancy, subclonal diversification, metastasis and resistance to therapy. Later stages of disease can be explored through analyzing multiple samples collected longitudinally, on or between successive treatments, and finally at time of autopsy. It is also possible to study earlier stages of cancer development through probabilistic reconstruction of developmental trajectories based on mutational information preserved in the genome. Here we present a suite of tools, called Phylogic N-Dimensional with Timing (PhylogicNDT), that statistically model phylogenetic and evolutionary trajectories based on mutation and copy-number data representing samples taken at single or multiple time points. PhylogicNDT can be used to infer: (i) the order of clonal driver events (including in pre-cancerous stages); (ii) subclonal populations of cells and their phylogenetic relationships; and (iii) cell population dynamics. We demonstrate the use of PhylogicNDT by applying it to whole-exome and whole-genome data of 498 lung adenocarcinoma samples (434 previously available and 64 of newly generated data). We identify significantly different progression trajectories across subtypes of lung adenocarcinoma (EGFR mutant, KRAS mutant, fusion-driven and EGFR/KRAS wild type cancers). In addition, we study the progression of fusion-driven lung cancer in 21 patients by analyzing samples from multiple timepoints during treatment with 1st and next generation tyrosine kinase inhibitors. We characterize their subclonal diversification, dynamics, selection, and changes in mutational signatures and neoantigen load. This methodology will enable a systematic study of tumour initiation, progression and resistance across cancer types and therapies.

bioinformatics↗